While graph convolution on DCRNN assumes a strong correlation between two sensors that are topologically-close, GCGRNN [1]} does not make the same assumption. It achieves state of the art performance in traffic state prediction by integrating data-driven graph filter and gated recurrent neural network to automatically learn the adjacency matrix and capture hidden correlations among traffic sensors. In addition, by using gated recurrent unit cells, GCGRNN enjoys better training efficiency.
